In recent years, the media industry has achieved rapid development and experienced three development stages from traditional media to new media and then to the current convergence media. Convergence media has brought about great changes in content production, communication mechanism, operation and maintenance management, but also brought about problems such as declining credibility of the industry, difficulty in confirming content rights, difficulty in protecting user privacy, etc. Research on convergence media based on blockchain can make use of the characteristics of blockchain to design or optimize the media industry. In this paper, we introduced the development of convergence media, blockchain and consensus mechanism, then we described a sustainable convergence media ecology based on blockchain. Furthermore, we designed and implemented a consensus mechanism named proof of efficiency (PoE). After analysis, PoE can provide high security and resist 51% resource attack, sybil attack, etc. The experimental results show that PoE has the characteristics of decentralization, strong consistency, low energy consumption, short average block generation time, high throughput and short block confirmation time; the consensus results of PoE can reflect the node’s ecological characteristics in convergence media which can stimulate the activity of nodes and better solve the generation of the Matthew effect.

Online news platforms have become users’ primary information sources. However, they focus on attracting users to click on the news and ignore whether the news triggers a sense of engagement, which could potentially reduce users’ participation in public events. Therefore, this study constructs four indicators by assessing user engagement to build an intelligent system to help platforms optimize their publishing strategies. First, this study defines user engagement evaluation as a classification task that divides user engagement into four indicators and proposes an extended LDA model based on user click–comment behavior (UCCB), using which the attractiveness of words in news headlines and content can be effectively represented. Second, this study proposes a deep user engagement evaluation (DUEE) model that integrates news attractiveness and multiple features in an attention-based deep neural network for user engagement evaluation. The DUEE model considers various elements that collectively determine the ability of the news to attract clicks and engagement. Third, the proposed model is compared with the baseline and state-of-the-art techniques, showing that it outperforms all existing methods. This study provides new research contributions and ideas for improving user engagement in online news evaluation.

The widespread popularity of digital technology has enabled the rapid dissemination of news. However, it has also led to the emergence of “fake news” and the development of a media ecosystem with serious prejudices. If early warnings about the source of fake news are received, this provides better outcomes in preventing its spread. Therefore, the issue of understanding and evaluating the credibility of media has received increasing attention. This work proposes a model of evaluating news media credibility called MiBeMC, which mimics the structure of human verification behavior in networks. Specifically, we first construct an intramodule information feature extractor to simulate the semantic analysis behavior of human information reading. Then, we design a similarity module to mimic the process of obtaining additional information. We also construct an aggregation module. This simulates human verification of correlated content. Finally, we apply regularized adversarial training strategy to train the MiBeMC model. The ablation study results demonstrate the effectiveness of MiBeMC. For the CLEF-task4 development and test dataset, the performance of the MiBeMC over state-of-the-art baseline methods is evaluated and found to be superior.

As a new factor of production, data element has profoundly changed our mode of production, lifestyle and social governance style. The sharing of a data element in the convergence media ecology can greatly improve the circulation of a data element and enhance the value of a data element; however, it may face problems such as insufficient sharing willingness, incomplete sharing circulation mechanism and inadequate implementation of the incentive mechanism. To solve these problems, this paper introduced the evolutionary game theory in the convergence media ecology and established the data-sharing model according to the characteristics of nodes. We analyzed the ecological node evolution path, evolutionary stable strategy and the corresponding state conditions in the model. Furthermore, we carried out the sampling experiment simulation, which verified the effectiveness of the research content in this paper. At the end of the article, we summarize and give some key factors to increase the willingness to participate in sharing in convergence media ecology. This paper enriched the research field of data element sharing in convergence media and explored the willingness and tendency of the participants. The research results can provide targeted suggestions for promoting the sharing of data elements in convergence media ecology.

At present, the media industry is in the rapid development of convergence media, and is undergoing the development process of gradually evolving from full coverage of new media to full integration of media. Blockchain is a distributed database technology, with decentralized, immutable, traceable and other characteristics. The convergence media ecology built on blockchain technology can delegate power to ordinary nodes in the ecology, which is expected to have a profound impact and change on the existing convergence media business model, content creation method and industry structure. Based on the special attributes of the media industry, the blockchain convergence media ecology uses the form of alliance chain in the early stage of establishment. The existing blockchain node management mechanism is universal and does not adapt to the characteristics of the media industry. In order to ensure the basic operation and maintenance of the blockchain convergence media ecology, this paper defines the participation and credit degree system of the ecological node. On this basis, a Node dynamic access and exit (NDAE) operation and maintenance mechanism based on node interaction and node integrity was established. Simulation experiments show that the NDAE mechanism can better handle the access, exit and forced exit of nodes, which ensures the security of the blockchain convergence media ecology to a certain extent.

Currently, the media industry is in the rapid development stage of media integration, which has brought about great changes in content production mode, presentation form, communication mechanism, operation and maintenance management, etc. At the same time, it is also faced with problems such as difficult information traceability, declining industry credibility, low data circulation quality and efficiency, difficult data security and user privacy protection, etc. Utilizing blockchain’s characteristics can solve these problems that the media industry is currently facing. This paper designs a convergence media ecology model based on blockchain (CMEM-BC), focusing on the basic elements of the model, node operation and maintenance system, node management mechanism, value circulation mechanism, and storage mechanism, trying to establish a decentralized, traceable, and immutable convergence media ecosystem. On this basis, this paper summarizes the ecological framework and ecological model of CMEM-BC. Finally, the paper describes the verification of the effectiveness of CMEM-BC in key links through simulation experiments, verifying that CMEM-BC has high originality and is more suitable for the application of convergence media ecology through model analysis and comparison.